The F1-score is a measure used to assess the quality of binary classification problems as well as problems with multiple binary labels or multiple classes.

F1-score = 1 is the best value (perfect precision and recall), and the worst value is 0.

If you are looking to select a model based on a balance between precision and recall, don’t miss out on assessing your F1-scores!


F1-score is defined as the harmonic mean of the precision and recall:

\[\begin{array}{rcl} \text{F1-score} & = & 2 * \dfrac{\text{Precision * Recall}}{\text{Precision + Recall}} \\ \end{array}\]

Note that precision and recall have the same relative contribution to the F1-score.

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